Particle swarm optimization algorithm based on semantic relations and its engineering applications

摘要：

Particle swarm optimization algorithm (PSO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slowly convergence speed, According to the semantic relations, an improved PSO algorithm has been proposed in this paper. In contrast with the tradition algorithm, the improved algorithm is added with a new operator to update crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on ontology technology. Particle swarm optimization can be applied to many engineering fields, taking the engineering applications- Traveling Salesman Problem (TSP) as example, Our experiments show accuracy of improved particle swarm algorithm that is superior to that obtained by the other classical versions, and competitive or better than the results achieved by the compared algorithm, this improved algorithm also can improve the searching efficiency.

关键词： particle swarm optimization algorithm semantic relation ontology technology engineering TSP

作者: Liangshan Shao Yuan Bai Yunfei Qiu Zhanwei Du

作者单位: College of Science ofLiaoning Technical University, Liaoning, China College of Computer Science and Technology, Jilin University, Changchun, China

会议类型: 国际会议

会议名称: International Symposium on Emergency Management 2011(ISEM‘2011)(第六届国际应急管理论坛暨中国(双法)应急管理专业委员会第七届年会)

会议地点: 北京

会议语种:英文

页码: 294-299

在线出版日期: 2011-11-19（万方平台首次上网日期，不代表论文的发表时间）